Method and apparatus for point cloud compression

ABSTRACT

Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the processing circuitry of the apparatus for point cloud encoding determines, from a point cloud, more than two candidate source points that are associated with a target point in a reconstructed geometry reconstructed from a compressed geometry image for the point cloud. Then the processing circuitry determines a color for the target point based on colors of the more than two candidate source points, and encodes texture of the point cloud with the target point having the determined color.

INCORPORATION BY REFERENCE

This present application claims the benefit of priority to U.S.Provisional Application No. 62/812,955, “TECHNIQUES AND APPARATUS FORDISTANCE-WEIGHTED COLOR TRANSFER FOR POINT CLOUD COMPRESSION” filed onMar. 1, 2019, which is incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure describes embodiments generally related to pointcloud compression.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Various technologies are developed to capture and represent world, suchas objects in the world, environments in the world, and the like in3-dimensional (3D) space. 3D representations of the world can enablemore immersive forms of interaction and communication. Point clouds canbe used as 3D representation of the world. A point cloud is a set ofpoints in a 3D space, each with associated attributes, e.g. color,material properties, texture information, intensity attributes,reflectivity attributes, motion related attributes, modality attributes,and various other attributes. Such point clouds may include largeamounts of data and may be costly and time-consuming to store andtransmit.

SUMMARY

Aspects of the disclosure provide methods and apparatuses for pointcloud compression and decompression. In some examples, an apparatus forpoint cloud compression/decompression includes processing circuitry. Forexample, the processing circuitry of the apparatus for point cloudencoding determines, from a point cloud, more than two candidate sourcepoints that are associated with a target point in a reconstructedgeometry reconstructed from a compressed geometry image for the pointcloud. Then the processing circuitry determines a color for the targetpoint based on colors of the more than two candidate source points, andencodes texture of the point cloud with the target point having thedetermined color.

In some embodiments, the processing circuitry determines, from the pointcloud, a first set of candidate source points that are nearest neighborsto the target point. Then, the processing circuitry determines, from thepoint cloud, a second set of candidate source points with the targetpoint being one of the nearest neighbors to each of the candidate sourcepoints in the second set. Further, the processing circuitry determinesthe first set of candidate source points that are N1 nearest neighborsto the target point, N1 being greater than 1. Then, the processingcircuitry determines the second set of candidate source points with thetarget point being one of N2 nearest neighbors to each of the candidatesource points in the second set, N2 being greater than 1.

Further, in some embodiments, the processing circuitry calculates afirst weighted color average based on colors of the first set ofcandidate source points, calculates a second weighted color averagebased on colors of the second set of candidate source points, anddetermines the color for the target point based on the first weightedcolor average and the second weighted color average.

In some embodiments, the processing circuitry weights a color of acandidate source point in the first set based on a distance from thecandidate source point to the target point. In an example, theprocessing circuitry weights the color of the candidate source point inthe first set with a weight that is proportion to an inverse of thedistance from the candidate source point to the target point. In anotherexample, the processing circuitry weights the color of the candidatesource point in the first set with a weight that is proportion to aninverse of an ascending function of the distance

Aspects of the disclosure also provide a non-transitorycomputer-readable medium storing instructions which when executed by acomputer for point cloud compression/decompression cause the computer toperform the method for point cloud compression/decompression.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, the nature, and various advantages of the disclosedsubject matter will be more apparent from the following detaileddescription and the accompanying drawings in which:

FIG. 1 is a schematic illustration of a simplified block diagram of acommunication system (100) in accordance with an embodiment.

FIG. 2 is a schematic illustration of a simplified block diagram of astreaming system (200) in accordance with an embodiment.

FIG. 3 shows a block diagram of an encoder (300) for encoding pointcloud frames, according to some embodiments.

FIG. 4 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments.

FIG. 5 is a schematic illustration of a simplified block diagram of avideo decoder in accordance with an embodiment.

FIG. 6 is a schematic illustration of a simplified block diagram of avideo encoder in accordance with an embodiment.

FIG. 7 shows a flow chart outlining a process example according to someembodiments of the disclosure.

FIG. 8 is a schematic illustration of a computer system in accordancewith an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

Aspects of the disclosure provide point cloud coding techniques,specifically using video-coding for point cloud compression (V-PCC). TheV-PCC scheme can utilize generic video codecs for point cloudcompression. The point cloud coding techniques in the present disclosurecan improves both lossless and lossy compression of missed pointsgenerated by the V-PCC.

A point cloud is a set of points in a 3D space, each with associatedattributes, e.g. color, material properties, texture information,intensity attributes, reflectivity attributes, motion relatedattributes, modality attributes, and various other attributes. Pointclouds can be used to reconstruct an object or a scene as a compositionof such points. The points can be captured using multiple cameras anddepth sensors in various setups and may be made up of thousands up tobillions of points in order to realistically represent reconstructedscenes.

Compression technologies are needed to reduce the amount of datarequired to represent a point cloud. As such, technologies are neededfor lossy compression of point clouds for use in real-timecommunications and six Degrees of Freedom (6 DoF) virtual reality. Inaddition, technology is sought for lossless point cloud compression inthe context of dynamic mapping for autonomous driving and culturalheritage applications, and the like. Moving picture experts group (MPEG)starts working on a standard to address compression of geometry andattributes such as colors and reflectance, scalable/progressive coding,coding of sequences of point clouds captured over time, and randomaccess to subsets of the point cloud.

According to an aspect of the disclosure, the main philosophy behindV-PCC is to leverage existing video codecs to compress the geometry,occupancy, and texture of a dynamic point cloud as three separate videosequences. The extra metadata needed to interpret the three videosequences are compressed separately. A small portion of the overallbitstream is the metadata, which could be encoded/decoded efficientlyusing software implementation. The bulk of the information is handled bythe video codec.

FIG. 1 illustrates a simplified block diagram of a communication system(100) according to an embodiment of the present disclosure. Thecommunication system (100) includes a plurality of terminal devices thatcan communicate with each other, via, for example, a network (150). Forexample, the communication system (100) includes a pair of terminaldevices (110) and (120) interconnected via the network (150). In theFIG. 1 example, the first pair of terminal devices (110) and (120) thatperform unidirectional transmission of point cloud data. For example,the terminal device (110) may compress point cloud (e.g., pointsrepresenting a structure) that are captured by a sensor 105 connectedwith the terminal device (110). The compressed point cloud can betransmitted, for example in the form of a bitstream, to the otherterminal device (120) via the network (150). The terminal device (120)may receive the compressed point cloud from the network (150),decompress the bitstream to reconstruct the point cloud and suitablydisplay according to the reconstructed point cloud. Unidirectional datatransmission may be common in media serving applications and the like.

In the FIG. 1 example, the terminal devices (110) and (120) may beillustrated as servers, and personal computers, but the principles ofthe present disclosure may be not so limited. Embodiments of the presentdisclosure find application with laptop computers, tablet computers,smart phones, gaming terminals, media players and/or dedicatedthree-dimensional (3D) equipment. The network (150) represents anynumber of networks that transmit compressed point cloud between theterminal devices (110) and (120). The network (150) can include forexample wireline (wired) and/or wireless communication networks. Thenetwork (150) may exchange data in circuit-switched and/orpacket-switched channels. Representative networks includetelecommunications networks, local area networks, wide area networksand/or the Internet. For the purposes of the present discussion, thearchitecture and topology of the network (150) may be immaterial to theoperation of the present disclosure unless explained herein below.

FIG. 2 illustrates, as an example for an application for the disclosedsubject matter for point cloud. The disclosed subject matter can beequally applicable to other point cloud enabled applications, including,3D telepresence application, virtual reality application.

A streaming system (200) may include a capture subsystem (213). Thecapture subsystem (213) can include a point cloud source (201), forexample light detection and ranging (LIDAR) systems, 3D cameras, 3Dscanners, a graphics generation component that generates theuncompressed point cloud in software, and like that generates forexample point clouds (202) that are uncompressed. In an example, thepoint clouds (202) include points that are captured by the 3D cameras.The point clouds (202), depicted as a bold line to emphasize a high datavolume when compared to compressed point clouds (204) (a bitstream ofcompressed point clouds). The compressed point clouds (204) can begenerated by an electronic device (220) that includes an encoder (203)coupled to the point cloud source (201). The encoder (203) can includehardware, software, or a combination thereof to enable or implementaspects of the disclosed subject matter as described in more detailbelow. The compressed point clouds (204) (or bitstream of compressedpoint clouds (204)), depicted as a thin line to emphasize the lower datavolume when compared to the stream of point clouds (202), can be storedon a streaming server (205) for future use. One or more streaming clientsubsystems, such as client subsystems (206) and (208) in FIG. 2 canaccess the streaming server (205) to retrieve copies (207) and (209) ofthe compressed point cloud (204). A client subsystem (206) can include adecoder (210), for example, in an electronic device (230). The decoder(210) decodes the incoming copy (207) of the compressed point clouds andcreates an outgoing stream of reconstructed point clouds (211) that canbe rendered on a rendering device (212). In some streaming systems, thecompressed point clouds (204), (207), and (209) (e.g., bitstreams ofcompressed point clouds) can be compressed according to certainstandards. In some examples, video coding standards are used in thecompression of point clouds. Examples of those standards include, HighEfficiency Video Coding (HEVC), Versatile Video Coding (VVC), and thelike.

It is noted that the electronic devices (220) and (230) can includeother components (not shown). For example, the electronic device (220)can include a decoder (not shown) and the electronic device (230) caninclude an encoder (not shown) as well.

FIG. 3 shows a block diagram of a V-PCC encoder (300) for encoding pointcloud frames, according to some embodiments. In some embodiments, theV-PCC encoder (300) can be used in the communication system (100) andstreaming system (200). For example, the encoder (203) can be configuredand operate in a similar manner as the V-PCC encoder (300).

The V-PCC encoder (300) receives point cloud frames that areuncompressed inputs and generates bitstream corresponding to compressedpoint cloud frames. In some embodiments, the V-PCC encoder (300) mayreceive the point cloud frames from a point cloud source, such as thepoint cloud source (201) and the like.

In the FIG. 3 example, the V-PCC encoder (300) includes a patchgeneration module (306), a patch packing module (308), a geometry imagegeneration module (310), a texture image generation module (312), apatch info module (304), an occupancy map module (314), a smoothingmodule (336), image padding modules (316) and (318), a group dilationmodule (320), video compression modules (322), (323) and (332), anauxiliary patch info compression module (338), an entropy compressionmodule (334) and a multiplexer (324) coupled together as shown in FIG.3.

According to an aspect of the disclosure, the V-PCC encoder (300),converts 3D point cloud frames into an image-based representation alongwith some meta data (e.g., occupancy map and patch info) necessary toconvert the compressed point cloud back into a decompressed point cloud.In some examples, the V-PCC encoder (300) can convert 3D point cloudframes into geometry images, texture images and occupancy maps, and thenuse video coding techniques to encode the geometry images, textureimages and occupancy maps into a bitstream. Generally, a geometry imageis a 2D image with pixels filled with geometry values associated withpoints projected to the pixels, and a pixel filled with a geometry valuecan be referred to as a geometry sample. A texture image is a 2D imagewith pixels filled with texture values associated with points projectedto the pixels and a pixel filled with texture value can be referred toas a texture sample. An occupancy map is a 2D image with pixels filledwith values that indicate occupied or unoccupied by patches.

The patch generation module (306) segments a point cloud into a set ofpatches (e.g., a patch is defined as a contiguous subset of the surfacedescribed by the point cloud), which may be overlapping or not, suchthat each patch may be described by a depth field with respect to aplane in 2D space. In some embodiments, the patch generation module(306) aims at decomposing the point cloud into a minimum number ofpatches with smooth boundaries, while also minimizing the reconstructionerror.

The patch info module (304) can collect the patch information thatindicates sizes and shapes of the patches. In some examples, the patchinformation can be packed into an image frame and then encoded by theauxiliary patch info compression module (338) to generate the compressedauxiliary patch information.

The patch packing module (308) is configured to map the extractedpatches onto a 2 dimensional (2D) grid while minimize the unused spaceand guarantee that every M×M (e.g., 16×16) block of the grid isassociated with a unique patch. Efficient patch packing can directlyimpact the compression efficiency either by minimizing the unused spaceor ensuring temporal consistency.

The geometry image generation module (310) can generate 2D geometryimages associated with geometry of the point cloud at given patchlocations. The texture image generation module (312) can generate 2Dtexture images associated with texture of the point cloud at given patchlocations. The geometry image generation module (310) and the textureimage generation module (312) exploit the 3D to 2D mapping computedduring the packing process to store the geometry and texture of thepoint cloud as images. In order to better handle the case of multiplepoints being projected to the same sample, each patch is projected ontotwo images, referred to as layers. In an example, geometry image isrepresented by a monochromatic frame of W×H in YUV420-8 bit format. Togenerate the texture image, the texture generation procedure exploitsthe reconstructed/smoothed geometry in order to compute the colors to beassociated with the re-sampled points.

The occupancy map module (314) can generate an occupancy map thatdescribes padding information at each unit. For example, the occupancyimage includes of a binary map that indicates for each cell of the gridwhether the cell belongs to the empty space or to the point cloud. In anexample, the occupancy map uses binary information describing for eachpixel whether the pixel is padded or not. In another example, theoccupancy map uses binary information describing for each block ofpixels whether the block of pixels is padded or not.

The occupancy map generated by the occupancy map module (314) can becompressed using lossless coding or lossy coding. When lossless codingis used, the entropy compression module (334) is used to compress theoccupancy map; when lossy coding is used, the video compression module(332) is used to compress the occupancy map.

It is noted that the patch packing module (308) may leave some emptyspaces between 2D patches packed in an image frame. The image paddingmodule (316) and (318) can fill the empty spaces (referred to aspadding) in order to generate an image frame that may be suited for 2Dvideo and image codecs. The image padding is also referred to asbackground filling which can fill the unused space by redundantinformation. In some examples, a good background filling minimallyincreases the bit rate while does not introduce significant codingdistortion around the patch boundaries.

The video compression modules (322), (323) and (332) can encode the 2Dimages, such as the padded geometry images, padded texture images, andoccupancy maps based on a suitable video coding standard, such as HEVC,VVC and the like. In an example, the video compression modules (322),(323) and (332) are individual components that operate separately. It isnoted that the video compression modules (322), (323) and (332) can beimplemented as a single component in another example.

In some examples, the smoothing module (336) is configured to generate asmoothed image of the reconstructed geometry image. The smoothed imageinformation can be provided to the texture image generation (312). Then,the texture image generation (312) may adjust the generation of thetexture image based on the reconstructed geometry images. For example,when a patch shape (e.g. geometry) is slightly distorted during encodingand decoding, the distortion may be taken into account when generatingthe texture images to correct for the distortion in patch shape.

In some embodiments, the group dilation (320) is configured to padpixels around the object boundaries with redundant low-frequency contentin order to improve coding gain as well as visual quality ofreconstructed point cloud.

The multiplexer (324) can multiplex the compressed geometry image, thecompressed texture image, the compressed occupancy map, the compressedauxiliary patch information into a compressed bitstream.

FIG. 4 shows a block diagram of a V-PCC decoder (400) for decodingcompressed bitstream corresponding to point cloud frames, according tosome embodiments. In some embodiments, the V-PCC decoder (400) can beused in the communication system (100) and streaming system (200). Forexample, the decoder (210) can be configured and operate in a similarmanner as the V-PCC decoder (400). The V-PCC decoder (400) receives thecompressed bitstream, and generates reconstructed point cloud based onthe compressed bitstream.

In the FIG. 4 example, the V-PCC decoder (400) includes a de-multiplexer(432), video decompression modules (434) and (436), an occupancy mapdecompression module (438), an auxiliary patch-information decompressionmodule (442), a geometry reconstruction module (444), a smoothing module(446), a texture reconstruction module (448) and a color smoothingmodule (452) coupled together as shown in FIG. 4.

The de-multiplexer (432) can receive the compressed bitstream andseparate into compressed texture image, compressed geometry image,compressed occupancy map and compressed auxiliary patch information.

The video decompression modules (434) and (436) can decode thecompressed images according to suitable standard (e.g., HEVC, VVC, etc.)and output decompressed images. For example, the video decompressionmodule (434) decodes the compressed texture images and outputsdecompressed texture images; and the video decompression module (436)decodes the compressed geometry images and outputs the decompressedgeometry images.

The occupancy map decompression module (438) can decode the compressedoccupancy maps according to suitable standard (e.g., HEVC, VVC, etc.)and output decompressed occupancy maps.

The auxiliary patch-information decompression module (442) can decodethe compressed auxiliary patch information according to suitablestandard (e.g., HEVC, VVC, etc.) and output decompressed auxiliary patchinformation.

The geometry reconstruction module (444) can receive the decompressedgeometry images, and generate reconstructed point cloud geometry basedon the decompressed occupancy map and decompressed auxiliary patchinformation.

The smoothing module (446) can smooth incongruences at edges of patches.The smoothing procedure aims at alleviating potential discontinuitiesthat may arise at the patch boundaries due to compression artifacts. Insome embodiments, a smoothing filter may be applied to the pixelslocated on the patch boundaries to alleviate the distortions that may becaused by the compression/decompression.

The texture reconstruction module (448) can determine textureinformation for points in the point cloud based on the decompressedtexture images and the smoothing geometry.

The color smoothing module (452) can smooth incongruences of coloring.Non-neighboring patches in 3D space are often packed next to each otherin 2D videos. In some examples, pixel values from non-neighboringpatches might be mixed up by the block-based video codec. The goal ofcolor smoothing is to reduce the visible artifacts that appear at patchboundaries.

FIG. 5 shows a block diagram of a video decoder (510) according to anembodiment of the present disclosure. The video decoder (510) can beused in the V-PCC decoder (400). For example, the video decompressionmodules (434) and (436), the occupancy map decompression module (438)can be similarly configured as the video decoder (510).

The video decoder (510) may include a parser (520) to reconstructsymbols (521) from compressed images, such as the coded video sequence.Categories of those symbols include information used to manage operationof the video decoder (510). The parser (520) may parse/entropy-decodethe coded video sequence that is received. The coding of the coded videosequence can be in accordance with a video coding technology orstandard, and can follow various principles, including variable lengthcoding, Huffman coding, arithmetic coding with or without contextsensitivity, and so forth. The parser (520) may extract from the codedvideo sequence, a set of subgroup parameters for at least one of thesubgroups of pixels in the video decoder, based upon at least oneparameter corresponding to the group. Subgroups can include Groups ofPictures (GOPs), pictures, tiles, slices, macroblocks, Coding Units(CUs), blocks, Transform Units (TUs), Prediction Units (PUs) and soforth. The parser (520) may also extract from the coded video sequenceinformation such as transform coefficients, quantizer parameter values,motion vectors, and so forth.

The parser (520) may perform an entropy decoding/parsing operation onthe video sequence received from a buffer memory, so as to createsymbols (521).

Reconstruction of the symbols (521) can involve multiple different unitsdepending on the type of the coded video picture or parts thereof (suchas: inter and intra picture, inter and intra block), and other factors.Which units are involved, and how, can be controlled by the subgroupcontrol information that was parsed from the coded video sequence by theparser (520). The flow of such subgroup control information between theparser (520) and the multiple units below is not depicted for clarity.

Beyond the functional blocks already mentioned, the video decoder (510)can be conceptually subdivided into a number of functional units asdescribed below. In a practical implementation operating undercommercial constraints, many of these units interact closely with eachother and can, at least partly, be integrated into each other. However,for the purpose of describing the disclosed subject matter, theconceptual subdivision into the functional units below is appropriate.

A first unit is the scaler/inverse transform unit (551). Thescaler/inverse transform unit (551) receives a quantized transformcoefficient as well as control information, including which transform touse, block size, quantization factor, quantization scaling matrices,etc. as symbol(s) (521) from the parser (520). The scaler/inversetransform unit (551) can output blocks comprising sample values, thatcan be input into aggregator (555).

In some cases, the output samples of the scaler/inverse transform (551)can pertain to an intra coded block; that is: a block that is not usingpredictive information from previously reconstructed pictures, but canuse predictive information from previously reconstructed parts of thecurrent picture. Such predictive information can be provided by an intrapicture prediction unit (552). In some cases, the intra pictureprediction unit (552) generates a block of the same size and shape ofthe block under reconstruction, using surrounding already reconstructedinformation fetched from the current picture buffer (558). The currentpicture buffer (558) buffers, for example, partly reconstructed currentpicture and/or fully reconstructed current picture. The aggregator(555), in some cases, adds, on a per sample basis, the predictioninformation the intra prediction unit (552) has generated to the outputsample information as provided by the scaler/inverse transform unit(551).

In other cases, the output samples of the scaler/inverse transform unit(551) can pertain to an inter coded, and potentially motion compensatedblock. In such a case, a motion compensation prediction unit (553) canaccess reference picture memory (557) to fetch samples used forprediction. After motion compensating the fetched samples in accordancewith the symbols (521) pertaining to the block, these samples can beadded by the aggregator (555) to the output of the scaler/inversetransform unit (551) (in this case called the residual samples orresidual signal) so as to generate output sample information. Theaddresses within the reference picture memory (557) from where themotion compensation prediction unit (553) fetches prediction samples canbe controlled by motion vectors, available to the motion compensationprediction unit (553) in the form of symbols (521) that can have, forexample X, Y, and reference picture components. Motion compensation alsocan include interpolation of sample values as fetched from the referencepicture memory (557) when sub-sample exact motion vectors are in use,motion vector prediction mechanisms, and so forth.

The output samples of the aggregator (555) can be subject to variousloop filtering techniques in the loop filter unit (556). Videocompression technologies can include in-loop filter technologies thatare controlled by parameters included in the coded video sequence (alsoreferred to as coded video bitstream) and made available to the loopfilter unit (556) as symbols (521) from the parser (520), but can alsobe responsive to meta-information obtained during the decoding ofprevious (in decoding order) parts of the coded picture or coded videosequence, as well as responsive to previously reconstructed andloop-filtered sample values.

The output of the loop filter unit (556) can be a sample stream that canbe output to a render device as well as stored in the reference picturememory (557) for use in future inter-picture prediction.

Certain coded pictures, once fully reconstructed, can be used asreference pictures for future prediction. For example, once a codedpicture corresponding to a current picture is fully reconstructed andthe coded picture has been identified as a reference picture (by, forexample, the parser (520)), the current picture buffer (558) can becomea part of the reference picture memory (557), and a fresh currentpicture buffer can be reallocated before commencing the reconstructionof the following coded picture.

The video decoder (510) may perform decoding operations according to apredetermined video compression technology in a standard, such as ITU-TRec. H.265. The coded video sequence may conform to a syntax specifiedby the video compression technology or standard being used, in the sensethat the coded video sequence adheres to both the syntax of the videocompression technology or standard and the profiles as documented in thevideo compression technology or standard. Specifically, a profile canselect certain tools as the only tools available for use under thatprofile from all the tools available in the video compression technologyor standard. Also necessary for compliance can be that the complexity ofthe coded video sequence is within bounds as defined by the level of thevideo compression technology or standard. In some cases, levels restrictthe maximum picture size, maximum frame rate, maximum reconstructionsample rate (measured in, for example megasamples per second), maximumreference picture size, and so on. Limits set by levels can, in somecases, be further restricted through Hypothetical Reference Decoder(HRD) specifications and metadata for HRD buffer management signaled inthe coded video sequence.

FIG. 6 shows a block diagram of a video encoder (603) according to anembodiment of the present disclosure. The video encoder (603) can beused in the V-PCC encoder (300) the compresses point clouds. In anexample, the video compression module (322) and (323), and the videocompression module (332) are configured similarly to the encoder (603).

The video encoder (603) may receive images, such as padded geometryimages, padded texture images and the like, and generate compressedimages.

According to an embodiment, the video encoder (603) may code andcompress the pictures of the source video sequence (images) into a codedvideo sequence (compressed images) in real time or under any other timeconstraints as required by the application. Enforcing appropriate codingspeed is one function of a controller (650). In some embodiments, thecontroller (650) controls other functional units as described below andis functionally coupled to the other functional units. The coupling isnot depicted for clarity. Parameters set by the controller (650) caninclude rate control related parameters (picture skip, quantizer, lambdavalue of rate-distortion optimization techniques, . . . ), picture size,group of pictures (GOP) layout, maximum motion vector search range, andso forth. The controller (650) can be configured to have other suitablefunctions that pertain to the video encoder (603) optimized for acertain system design.

In some embodiments, the video encoder (603) is configured to operate ina coding loop. As an oversimplified description, in an example, thecoding loop can include a source coder (630) (e.g., responsible forcreating symbols, such as a symbol stream, based on an input picture tobe coded, and a reference picture(s)), and a (local) decoder (633)embedded in the video encoder (603). The decoder (633) reconstructs thesymbols to create the sample data in a similar manner as a (remote)decoder also would create (as any compression between symbols and codedvideo bitstream is lossless in the video compression technologiesconsidered in the disclosed subject matter). The reconstructed samplestream (sample data) is input to the reference picture memory (634). Asthe decoding of a symbol stream leads to bit-exact results independentof decoder location (local or remote), the content in the referencepicture memory (634) is also bit exact between the local encoder andremote encoder. In other words, the prediction part of an encoder “sees”as reference picture samples exactly the same sample values as a decoderwould “see” when using prediction during decoding. This fundamentalprinciple of reference picture synchronicity (and resulting drift, ifsynchronicity cannot be maintained, for example because of channelerrors) is used in some related arts as well.

The operation of the “local” decoder (633) can be the same as of a“remote” decoder, such as the video decoder (510), which has alreadybeen described in detail above in conjunction with FIG. 5. Brieflyreferring also to FIG. 5, however, as symbols are available andencoding/decoding of symbols to a coded video sequence by an entropycoder (645) and the parser (520) can be lossless, the entropy decodingparts of the video decoder (510), including and parser (520) may not befully implemented in the local decoder (633).

An observation that can be made at this point is that any decodertechnology except the parsing/entropy decoding that is present in adecoder also necessarily needs to be present, in substantially identicalfunctional form, in a corresponding encoder. For this reason, thedisclosed subject matter focuses on decoder operation. The descriptionof encoder technologies can be abbreviated as they are the inverse ofthe comprehensively described decoder technologies. Only in certainareas a more detail description is required and provided below.

During operation, in some examples, the source coder (630) may performmotion compensated predictive coding, which codes an input picturepredictively with reference to one or more previously-coded picture fromthe video sequence that were designated as “reference pictures”. In thismanner, the coding engine (632) codes differences between pixel blocksof an input picture and pixel blocks of reference picture(s) that may beselected as prediction reference(s) to the input picture.

The local video decoder (633) may decode coded video data of picturesthat may be designated as reference pictures, based on symbols createdby the source coder (630). Operations of the coding engine (632) mayadvantageously be lossy processes. When the coded video data may bedecoded at a video decoder (not shown in FIG. 6), the reconstructedvideo sequence typically may be a replica of the source video sequencewith some errors. The local video decoder (633) replicates decodingprocesses that may be performed by the video decoder on referencepictures and may cause reconstructed reference pictures to be stored inthe reference picture cache (634). In this manner, the video encoder(603) may store copies of reconstructed reference pictures locally thathave common content as the reconstructed reference pictures that will beobtained by a far-end video decoder (absent transmission errors).

The predictor (635) may perform prediction searches for the codingengine (632). That is, for a new picture to be coded, the predictor(635) may search the reference picture memory (634) for sample data (ascandidate reference pixel blocks) or certain metadata such as referencepicture motion vectors, block shapes, and so on, that may serve as anappropriate prediction reference for the new pictures. The predictor(635) may operate on a sample block-by-pixel block basis to findappropriate prediction references. In some cases, as determined bysearch results obtained by the predictor (635), an input picture mayhave prediction references drawn from multiple reference pictures storedin the reference picture memory (634).

The controller (650) may manage coding operations of the source coder(630), including, for example, setting of parameters and subgroupparameters used for encoding the video data.

Output of all aforementioned functional units may be subjected toentropy coding in the entropy coder (645). The entropy coder (645)translates the symbols as generated by the various functional units intoa coded video sequence, by lossless compressing the symbols according totechnologies such as Huffman coding, variable length coding, arithmeticcoding, and so forth.

The controller (650) may manage operation of the video encoder (603).During coding, the controller (650) may assign to each coded picture acertain coded picture type, which may affect the coding techniques thatmay be applied to the respective picture. For example, pictures oftenmay be assigned as one of the following picture types:

An Intra Picture (I picture) may be one that may be coded and decodedwithout using any other picture in the sequence as a source ofprediction. Some video codecs allow for different types of intrapictures, including, for example Independent Decoder Refresh (“IDR”)Pictures. A person skilled in the art is aware of those variants of Ipictures and their respective applications and features.

A predictive picture (P picture) may be one that may be coded anddecoded using intra prediction or inter prediction using at most onemotion vector and reference index to predict the sample values of eachblock.

A bi-directionally predictive picture (B Picture) may be one that may becoded and decoded using intra prediction or inter prediction using atmost two motion vectors and reference indices to predict the samplevalues of each block. Similarly, multiple-predictive pictures can usemore than two reference pictures and associated metadata for thereconstruction of a single block.

Source pictures commonly may be subdivided spatially into a plurality ofsample blocks (for example, blocks of 4×4, 8×8, 4×8, or 16×16 sampleseach) and coded on a block-by-block basis. Blocks may be codedpredictively with reference to other (already coded) blocks asdetermined by the coding assignment applied to the blocks' respectivepictures. For example, blocks of I pictures may be codednon-predictively or they may be coded predictively with reference toalready coded blocks of the same picture (spatial prediction or intraprediction). Pixel blocks of P pictures may be coded predictively, viaspatial prediction or via temporal prediction with reference to onepreviously coded reference picture. Blocks of B pictures may be codedpredictively, via spatial prediction or via temporal prediction withreference to one or two previously coded reference pictures.

The video encoder (603) may perform coding operations according to apredetermined video coding technology or standard, such as ITU-T Rec.H.265. In its operation, the video encoder (603) may perform variouscompression operations, including predictive coding operations thatexploit temporal and spatial redundancies in the input video sequence.The coded video data, therefore, may conform to a syntax specified bythe video coding technology or standard being used.

A video may be in the form of a plurality of source pictures (images) ina temporal sequence. Intra-picture prediction (often abbreviated tointra prediction) makes use of spatial correlation in a given picture,and inter-picture prediction makes uses of the (temporal or other)correlation between the pictures. In an example, a specific pictureunder encoding/decoding, which is referred to as a current picture, ispartitioned into blocks. When a block in the current picture is similarto a reference block in a previously coded and still buffered referencepicture in the video, the block in the current picture can be coded by avector that is referred to as a motion vector. The motion vector pointsto the reference block in the reference picture, and can have a thirddimension identifying the reference picture, in case multiple referencepictures are in use.

In some embodiments, a bi-prediction technique can be used in theinter-picture prediction. According to the bi-prediction technique, tworeference pictures, such as a first reference picture and a secondreference picture that are both prior in decoding order to the currentpicture in the video (but may be in the past and future, respectively,in display order) are used. A block in the current picture can be codedby a first motion vector that points to a first reference block in thefirst reference picture, and a second motion vector that points to asecond reference block in the second reference picture. The block can bepredicted by a combination of the first reference block and the secondreference block.

Further, a merge mode technique can be used in the inter-pictureprediction to improve coding efficiency.

According to some embodiments of the disclosure, predictions, such asinter-picture predictions and intra-picture predictions are performed inthe unit of blocks. For example, according to the HEVC standard, apicture in a sequence of video pictures is partitioned into coding treeunits (CTU) for compression, the CTUs in a picture have the same size,such as 64×64 pixels, 32×32 pixels, or 16×16 pixels. In general, a CTUincludes three coding tree blocks (CTBs), which are one luma CTB and twochroma CTBs. Each CTU can be recursively quadtree split into one ormultiple coding units (CUs). For example, a CTU of 64×64 pixels can besplit into one CU of 64×64 pixels, or 4 CUs of 32×32 pixels, or 16 CUsof 16×16 pixels. In an example, each CU is analyzed to determine aprediction type for the CU, such as an inter prediction type or an intraprediction type. The CU is split into one or more prediction units (PUs)depending on the temporal and/or spatial predictability. Generally, eachPU includes a luma prediction block (PB), and two chroma PBs. In anembodiment, a prediction operation in coding (encoding/decoding) isperformed in the unit of a prediction block. Using a luma predictionblock as an example of a prediction block, the prediction block includesa matrix of values (e.g., luma values) for pixels, such as 8×8 pixels,16×16 pixels, 8×16 pixels, 16×8 pixels, and the like.

As described above, in some embodiments, a V-PCC encoder can firstcompress the geometry image and then the color data. For example, thecompressed geometry is first reconstructed and then the colors from theuncompressed (a.k.a source) point cloud are transferred to thereconstructed geometry, and the transferred color is compressed. Thebetter the color transfer, the better the compressed texture quality.This disclosure proposes techniques for the color transfer.

The proposed methods may be used separately or combined in any order.Further, each of the methods (or embodiments), encoder, and decoder maybe implemented by processing circuitry (e.g., one or more processors orone or more integrated circuits). In one example, the one or moreprocessors execute a program that is stored in a non-transitorycomputer-readable medium.

In a related example, a V-PCC encoder can find two candidate sourcepoints in the original point cloud for a target point in thereconstructed geometry and compute a centroid of the two candidatesource points as the color transferred to the target point. The presentdisclosure provides techniques use a larger number of candidate sourcepoints for color transfer to the target point. In some examples, thetechniques can be used to compute a weighted centroid where the weightsare computed according to the distances of the candidate source pointsto the target point.

In some embodiments, two sets of candidate source points are derived. Insome examples, each of the two sets is not empty, and at least one ofthe two sets has more than one candidate source point. For example,p_(i) ^((t)) denotes the i-th (current) target point. The first set ofcandidate source points is denoted by Ψ₁, and includes N₁ nearest sourceneighbors (source points that are neighbors) to p_(i) ^((t)), and acondition 1<N₁ is used to constrain the number of candidate sourcepoints in the first set. The second set of candidate points is denotedby Ψ₂, and includes any specific source point that p_(i) ^((t)) belongsto a neighbor set of N₂ nearest neighbors of the specific source point,and a condition 1<N₂ is used to constrain the number of nearestneighbors to the specific source point in the neighbor set.

Further, in some embodiments, a weighted color average is computed foreach set of candidate source points. The weighted color average can beused to as a collective candidate color for the set. Further, the twocollective candidate colors of the two sets can be used to calculate thecolor to transfer to the target point. In an example, the weighted coloraverage can replace the single candidate color used in the relatedexample. For example, an average of the two collective candidate colorsrespectively for the two sets can be used as the color to transfer tothe target point.

Specifically, in some examples, a Euclidian distance between points aand b is denoted by Δ(a,b). Then, the weighted color average of thecandidate set Ψ_(k) (k=1, 2) can be calculated by (Eq. 1):

$\begin{matrix}{{\overset{\_}{\Psi}}_{k} = \frac{\sum_{q \in \Psi_{k}}\frac{c(q)}{\Delta \left( {q,p_{i}^{(t)}} \right)}}{\sum_{q \in \Psi_{k}}\frac{1}{\Delta \left( {q,p_{i}^{(t)}} \right)}}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

where c(q) denotes the color of point q.

According to an aspect of the disclosure, an ascending function ofΔ(a,b) can be used in place of the Euclidian distance (a,b) in (Eq. 1).For example, f(x) denotes an ascending function of x, then the weightedcolor average can be calculated according to (Eq. 2):

$\begin{matrix}{{\overset{\_}{\Psi}}_{k} = \frac{\sum_{q \in \Psi_{k}}\frac{c(q)}{f\left( {\Delta \left( {q,p_{i}^{(t)}} \right)} \right)}}{\sum_{q \in \Psi_{k}}\frac{1}{f\left( {\Delta \left( {q,p_{i}^{(t)}} \right)} \right)}}} & \left( {{Eq}.\mspace{14mu} 2} \right)\end{matrix}$

FIG. 7 shows a flow chart outlining a process (700) according to anembodiment of the disclosure. The process (700) can be used during anencoding process for encoding point clouds. In various embodiments, theprocess (700) is executed by processing circuitry, such as theprocessing circuitry in the terminal devices (110), the processingcircuitry that performs functions of the encoder (203), the processingcircuitry that performs functions of the encoder (300), and the like. Insome embodiments, the process (700) is implemented in softwareinstructions, thus when the processing circuitry executes the softwareinstructions, the processing circuitry performs the process (700). Theprocess starts at (S701) and proceeds to (S710).

At (S710), more than two candidate source points that are associatedwith a target point are determined from a point cloud. The target pointis a point in reconstructed geometry of the point cloud. Thereconstructed geometry is reconstructed from a compressed geometry imagefor the point cloud. In some embodiments, a first set of candidatesource points and a second set of candidate source points are determinedfrom the point cloud. The first set of candidate source points includesnearest neighbors to the target point. In an example, the first set ofthe candidate source points includes N1 nearest neighbors to the targetpoint, and N1 is an integer that is greater than 1. For the second set,the target point is among the nearest neighbors to each of the candidatesource points in the second set. In an example, the target point isamong the N2 nearest neighbors to each of the candidate source point inthe second set, and N2 is an integer that is greater than 1.

At (S720), determine a color for the target point based on colors of themore than two candidate source points. In some examples, a firstweighted color average is calculated based on colors of the first set ofcandidate source points and a second weighted color average iscalculated based on colors of the second set of candidate source points.Then, the color for the target point is determined based on the firstweighted color average and the second weighted color average. In someembodiments, to calculate the first weighted color average and thesecond weighted color average, a color of a candidate source point canbe weighted based on a distance from the candidate source point to thetarget point. In an embodiment, the color of the candidate source pointis weighted by a weight that is proportion to an inverse of the distancefrom the candidate source point to the target point, such as using (Eq.1). In another embodiment, the color of the candidate source point witha weight that is proportion to an inverse of an ascending function ofthe distance, such as using (Eq. 2).

At (S730), texture for the point cloud is encoded. For example, in atexture image, the target point has the determined color. Then, theprocess proceeds to (S799) and terminates.

The techniques described above, can be implemented as computer softwareusing computer-readable instructions and physically stored in one ormore computer-readable media. For example, FIG. 8 shows a computersystem (800) suitable for implementing certain embodiments of thedisclosed subject matter.

The computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code comprising instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by one or more computer central processingunits (CPUs), Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 8 for computer system (800) are exemplaryin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementingembodiments of the present disclosure. Neither should the configurationof components be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary embodiment of a computer system (800).

Computer system (800) may include certain human interface input devices.Such a human interface input device may be responsive to input by one ormore human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): keyboard (801), mouse (802), trackpad (803), touchscreen (810), data-glove (not shown), joystick (805), microphone (806),scanner (807), camera (808).

Computer system (800) may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouch-screen (810), data-glove (not shown), or joystick (805), but therecan also be tactile feedback devices that do not serve as inputdevices), audio output devices (such as: speakers (809), headphones (notdepicted)), visual output devices (such as screens (810) to include CRTscreens, LCD screens, plasma screens, OLED screens, each with or withouttouch-screen input capability, each with or without tactile feedbackcapability—some of which may be capable to output two dimensional visualoutput or more than three dimensional output through means such asstereographic output; virtual-reality glasses (not depicted),holographic displays and smoke tanks (not depicted)), and printers (notdepicted).

Computer system (800) can also include human accessible storage devicesand their associated media such as optical media including CD/DVD ROM/RW(820) with CD/DVD or the like media (821), thumb-drive (822), removablehard drive or solid state drive (823), legacy magnetic media such astape and floppy disc (not depicted), specialized ROM/ASIC/PLD baseddevices such as security dongles (not depicted), and the like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

Computer system (800) can also include an interface to one or morecommunication networks. Networks can for example be wireless, wireline,optical. Networks can further be local, wide-area, metropolitan,vehicular and industrial, real-time, delay-tolerant, and so on. Examplesof networks include local area networks such as Ethernet, wireless LANs,cellular networks to include GSM, 3G, 4G, 5G, LTE and the like, TVwireline or wireless wide area digital networks to include cable TV,satellite TV, and terrestrial broadcast TV, vehicular and industrial toinclude CANBus, and so forth. Certain networks commonly require externalnetwork interface adapters that attached to certain general purpose dataports or peripheral buses (849) (such as, for example USB ports of thecomputer system (800)); others are commonly integrated into the core ofthe computer system (800) by attachment to a system bus as describedbelow (for example Ethernet interface into a PC computer system orcellular network interface into a smartphone computer system). Using anyof these networks, computer system (800) can communicate with otherentities. Such communication can be uni-directional, receive only (forexample, broadcast TV), uni-directional send-only (for example CANbus tocertain CANbus devices), or bi-directional, for example to othercomputer systems using local or wide area digital networks. Certainprotocols and protocol stacks can be used on each of those networks andnetwork interfaces as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces can be attached to a core (840) of thecomputer system (800).

The core (840) can include one or more Central Processing Units (CPU)(841), Graphics Processing Units (GPU) (842), specialized programmableprocessing units in the form of Field Programmable Gate Areas (FPGA)(843), hardware accelerators for certain tasks (844), and so forth.These devices, along with Read-only memory (ROM) (845), Random-accessmemory (846), internal mass storage such as internal non-user accessiblehard drives, SSDs, and the like (847), may be connected through a systembus (848). In some computer systems, the system bus (848) can beaccessible in the form of one or more physical plugs to enableextensions by additional CPUs, GPU, and the like. The peripheral devicescan be attached either directly to the core's system bus (848), orthrough a peripheral bus (849). Architectures for a peripheral businclude PCI, USB, and the like.

CPUs (841), GPUs (842), FPGAs (843), and accelerators (844) can executecertain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in ROM(845) or RAM (846). Transitional data can be also be stored in RAM(846), whereas permanent data can be stored for example, in the internalmass storage (847). Fast storage and retrieve to any of the memorydevices can be enabled through the use of cache memory, that can beclosely associated with one or more CPU (841), GPU (842), mass storage(847), ROM (845), RAM (846), and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of the present disclosure, or they can be of the kind wellknown and available to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system havingarchitecture (800), and specifically the core (840) can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core (840) that are of non-transitorynature, such as core-internal mass storage (847) or ROM (845). Thesoftware implementing various embodiments of the present disclosure canbe stored in such devices and executed by core (840). Acomputer-readable medium can include one or more memory devices orchips, according to particular needs. The software can cause the core(840) and specifically the processors therein (including CPU, GPU, FPGA,and the like) to execute particular processes or particular parts ofparticular processes described herein, including defining datastructures stored in RAM (846) and modifying such data structuresaccording to the processes defined by the software. In addition or as analternative, the computer system can provide functionality as a resultof logic hardwired or otherwise embodied in a circuit (for example:accelerator (844)), which can operate in place of or together withsoftware to execute particular processes or particular parts ofparticular processes described herein. Reference to software canencompass logic, and vice versa, where appropriate. Reference to acomputer-readable media can encompass a circuit (such as an integratedcircuit (IC)) storing software for execution, a circuit embodying logicfor execution, or both, where appropriate. The present disclosureencompasses any suitable combination of hardware and software.

APPENDIX A: ACRONYMS

JEM: joint exploration model

VVC: versatile video coding

BMS: benchmark set

MV: Motion Vector

HEVC: High Efficiency Video Coding

SEI: Supplementary Enhancement Information

VUI: Video Usability Information

GOPs: Groups of Pictures

TUs: Transform Units,

PUs: Prediction Units

CTUs: Coding Tree Units

CTBs: Coding Tree Blocks

PBs: Prediction Blocks

HRD: Hypothetical Reference Decoder

SNR: Signal Noise Ratio

CPUs: Central Processing Units

GPUs: Graphics Processing Units

CRT: Cathode Ray Tube

LCD: Liquid-Crystal Display

OLED: Organic Light-Emitting Diode

CD: Compact Disc

DVD: Digital Video Disc

ROM: Read-Only Memory

RAM: Random Access Memory

ASIC: Application-Specific Integrated Circuit

PLD: Programmable Logic Device

LAN: Local Area Network

GSM: Global System for Mobile communications

LTE: Long-Term Evolution

CANBus: Controller Area Network Bus

USB: Universal Serial Bus

PCI: Peripheral Component Interconnect

FPGA: Field Programmable Gate Areas

SSD: solid-state drive

IC: Integrated Circuit

CU: Coding Unit

While this disclosure has described several exemplary embodiments, thereare alterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods which, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

What is claimed is:
 1. A method for point cloud encoding, comprising:determining, from a point cloud, more than two candidate source pointsthat are associated with a target point in a reconstructed geometryreconstructed from a compressed geometry image for the point cloud;determining a color for the target point based on colors of the morethan two candidate source points; and encoding texture of the pointcloud with the target point having the determined color.
 2. The methodof claim 1, further comprising: determining, from the point cloud, afirst set of candidate source points that are nearest neighbors to thetarget point.
 3. The method of claim 2, further comprising: determining,from the point cloud, a second set of candidate source points with thetarget point being one of the nearest neighbors to each of the candidatesource points in the second set.
 4. The method of claim 2, furthercomprising: determining the first set of candidate source points thatare N1 nearest neighbors to the target point, N1 being greater than 1.5. The method of claim 3, further comprising: determining the second setof candidate source points with the target point being one of N2 nearestneighbors to each of the candidate source points in the second set, N2being greater than
 1. 6. The method of claim 3, further comprising:calculating a first weighted color average based on colors of the firstset of candidate source points; calculating a second weighted coloraverage based on colors of the second set of candidate source points;and determining the color for the target point based on the firstweighted color average and the second weighted color average.
 7. Themethod of claim 6, further comprising: weighting a color of a candidatesource point in the first set based on a distance from the candidatesource point to the target point.
 8. The method of claim 7, furthercomprising: weighting the color of the candidate source point in thefirst set with a weight that is proportion to an inverse of the distancefrom the candidate source point to the target point.
 9. The method ofclaim 7, further comprising: weighting the color of the candidate sourcepoint in the first set with a weight that is proportion to an inverse ofan ascending function of the distance.
 10. An apparatus for point cloudencoding, comprising: processing circuitry configured to: determine,from a point cloud, more than two candidate source points that areassociated with a target point in a reconstructed geometry reconstructedfrom a compressed geometry image for the point cloud; determine a colorfor the target point based on colors of the more than two candidatesource points; and encode texture of the point cloud with the targetpoint having the determined color.
 11. The apparatus of claim 10,wherein the processing circuitry is configured to: determine, from thepoint cloud, a first set of candidate source points that are nearestneighbors to the target point.
 12. The apparatus of claim 11, whereinthe processing circuitry is configured to: determine, from the pointcloud, a second set of candidate source points with the target pointbeing one of the nearest neighbors to each of the candidate sourcepoints in the second set.
 13. The apparatus of claim 11, wherein theprocessing circuitry is configured to: determine the first set ofcandidate source points that are N1 nearest neighbors to the targetpoint, N1 being greater than
 1. 14. The apparatus of claim 12, whereinthe processing circuitry is configured to: determine the second set ofcandidate source points with the target point being one of N2 nearestneighbors to each of the candidate source points in the second set, N2being greater than
 1. 15. The apparatus of claim 12, wherein theprocessing circuitry is configured to: calculate a first weighted coloraverage based on colors of the first set of candidate source points;calculate a second weighted color average based on colors of the secondset of candidate source points; and determine the color for the targetpoint based on the first weighted color average and the second weightedcolor average.
 16. The apparatus of claim 15, wherein the processingcircuitry is configured to: weight a color of a candidate source pointin the first set based on a distance from the candidate source point tothe target point.
 17. The apparatus of claim 16, wherein the processingcircuitry is configured to: weight the color of the candidate sourcepoint in the first set with a weight that is proportion to an inverse ofthe distance from the candidate source point to the target point. 18.The apparatus of claim 16, wherein the processing circuitry isconfigured to: weight the color of the candidate source point in thefirst set with a weight that is proportion to an inverse of an ascendingfunction of the distance.
 19. A non-transitory computer-readable mediumstoring instructions which when executed by a computer for point cloudencoding cause the computer to perform: determining, from a point cloud,more than two candidate source points that are associated with a targetpoint in a reconstructed geometry reconstructed from a compressedgeometry image for the point cloud; determining a color for the targetpoint based on colors of the more than two candidate source points; andencoding texture of the point cloud with the target point having thedetermined color.
 20. The non-transitory computer-readable medium ofclaim 19, wherein the instructions cause the computer to furtherperform: determining, from the point cloud, a first set of candidatesource points that are nearest neighbors to the target point; anddetermining, from the point cloud, a second set of candidate sourcepoints with the target point being one of the nearest neighbors to eachof the candidate source points in the second set.